Scaling tree-based automated machine learning to biomedical big data with a feature set selector
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[1] Wolfgang Banzhaf,et al. Genetic Programming: An Introduction , 1997 .
[2] Jianhua Li,et al. A Novel Image Classification Method with CNN-XGBoost Model , 2017, IWDW.
[3] David A Lewis,et al. A molecular signature of depression in the amygdala. , 2009, The American journal of psychiatry.
[4] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[5] Joaquin Vanschoren,et al. GAMA: Genetic Automated Machine learning Assistant , 2019, J. Open Source Softw..
[6] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[7] Randal S. Olson,et al. Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool , 2016, GPTP.
[8] Bill C. White,et al. Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure , 2015, BioData Mining.
[9] M. Kaul,et al. Modulation of glucocorticoid receptor nuclear translocation in neurons by immunophilins FKBP51 and FKBP52: Implications for major depressive disorder , 2009, Brain Research.
[10] Aaron Klein,et al. Efficient and Robust Automated Machine Learning , 2015, NIPS.
[11] Fernando Pérez-González,et al. Digital-Forensics and Watermarking , 2014, Lecture Notes in Computer Science.
[12] Marc Parizeau,et al. DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..
[13] Jason H. Moore,et al. STatistical Inference Relief (STIR) feature selection , 2018, bioRxiv.
[14] Lars Kotthoff,et al. Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA , 2017, J. Mach. Learn. Res..
[15] V. Zakuth,et al. Increased apoptosis in patients with major depression: A preliminary study. , 1999, Journal of immunology.
[16] Peter A. Flach,et al. Advances in Neural Information Processing Systems 28 , 2015 .
[17] Francis R. Bach,et al. Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression , 2016, J. Mach. Learn. Res..
[18] Randal S. Olson,et al. Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming , 2017, GECCO.
[19] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[20] David J. Montana,et al. Strongly Typed Genetic Programming , 1995, Evolutionary Computation.
[21] Randal S. Olson,et al. PMLB: a large benchmark suite for machine learning evaluation and comparison , 2017, BioData Mining.
[22] Michael Snyder,et al. High-Coverage Whole-Exome Sequencing Identifies Candidate Genes for Suicide in Victims with Major Depressive Disorder , 2017, Scientific Reports.
[23] Long Chen,et al. Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation , 2017 .
[24] Randal S. Olson,et al. Data-driven advice for applying machine learning to bioinformatics problems , 2017, PSB.
[25] Bill C. White,et al. Identification and replication of RNA-Seq gene network modules associated with depression severity , 2018, Translational Psychiatry.
[26] Masahiko Watanabe,et al. Distribution of Caskin1 protein and phenotypic characterization of its knockout mice using a comprehensive behavioral test battery , 2018, Molecular Brain.
[27] Naomi R. Wray,et al. Genetic Studies of Major Depressive Disorder: Why Are There No Genome-wide Association Study Findings and What Can We Do About It? , 2014, Biological Psychiatry.
[28] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[29] Trang T. Le,et al. Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients , 2018, AMIA.
[30] Nando de Freitas,et al. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.
[31] Randal S. Olson,et al. Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science , 2016, GECCO.
[32] Thomas Meitinger,et al. Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment , 2004, Nature Genetics.
[33] Y. Forsell,et al. Variations in FKBP5 and BDNF genes are suggestively associated with depression in a Swedish population-based cohort. , 2010, Journal of affective disorders.
[34] G. MacQueen,et al. A meta-analysis examining clinical predictors of hippocampal volume in patients with major depressive disorder. , 2009, Journal of psychiatry & neuroscience : JPN.
[35] A L Oberg,et al. An interaction quantitative trait loci tool implicates epistatic functional variants in an apoptosis pathway in smallpox vaccine eQTL data , 2016, Genes and Immunity.
[36] Rui Mei,et al. Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing , 2013, Molecular Psychiatry.